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DOE OSTI2013

Business Case for a Micro-Combined Heat and Power Fuel Cell System in Commercial Applications

Combined heat and power fuel cell systems (CHP-FCSs) provide consistent electrical power and hot water with greater efficiency and lower emissions than alternative sources. These systems can be used either as baseload, grid-connected, or as off-the-grid power sources. This report presents a business case for CHP-FCSs in the range of 5 to 50 kWe. Systems in this power range are considered micro-CHP-FCS. For this particular business case, commercial applications rather than residential or industrial are targeted. To understand the benefits of implementing a micro-CHP-FCS, the characteristics that determine their competitive advantage must first be identified. Locations with high electricity prices and low natural gas prices are ideal locations for micro-CHP-FCSs. Fortunately, these high spark spread locations are generally in the northeastern area of the United States and California where government incentives are already in place to offset the current high cost of the micro-CHP-FCSs. As a result of the inherently high efficiency of a fuel cell and their ability to use the waste heat that is generated as a CHP, they have higher efficiency. This results in lower fuel costs than comparable alternative small-scale power systems (e.g., microturbines and reciprocating engines). A variety of markets should consider micro-CHP-FCSs including those that require both heat and baseload electricity throughout the year. In addition, the reliable power of micro-CHP-FCSs could be beneficial to markets where electrical outages are especially frequent or costly. Greenhouse gas emission levels from micro-CHP-FCSs are 69 percent lower, and the human health costs are 99.9 percent lower, than those attributed to conventional coal-fired power plants. As a result, FCSs can allow a company to advertise as environmentally conscious and provide a bottom-line sales advantage. As a new technology in the early stages of adoption, micro-CHP-FCSs are currently more expensive than alternative technologies. As the technology gains a foothold in its target markets and demand increases, the costs will decline in response to improved manufacturing efficiencies, similar to trends seen with other technologies. Transparency Market Research forecasts suggest that the CHP-FCS market will grow at a compound annual growth rate of greater than 27 percent over the next 5 years. These production level increases, coupled with the expected low price of natural gas, indicate the economic payback period will move to less than 5 years over the course of the next 5 years. To better understand the benefits of micro-CHP-FCSs, The U.S. Department of Energy worked with ClearEdge Power to install fifteen 5-kWe fuel cells in the commercial markets of California and Oregon. Pacific Northwest National Laboratory is evaluating these systems in terms of economics, operations, and their environmental impact in real-world applications. As expected, the economic analysis has indicated that the high capital cost of the micro-CHP-FCSs results in a longer payback period than typically is acceptable for all but early-adopter market segments. However, a payback period of less than 3 years may be expected as increased production brings system cost down, and CHP incentives are maintained or improved.

CHP, business case↗
DOE OSTI2013

Transactive Control and Coordination of Distributed Assets for Ancillary Services

The need to diversify energy supplies, the need to mitigate energy-related environmental impact, and the entry of electric vehicles in large numbers present challenges and opportunities to power system professionals. Wind and solar power provide many benefits, and to reap the benefits the resulting increased variability—forecasted as well as unforecasted—should be addressed. A majority of states and the District of Columbia, representing over half of the total load, have passed renewable portfolio standards. California’s plans call for 33% renewable energy by 2020. For grid balancing and for meeting reliability standards, ancillary services are needed. The needs for these services are poised to increase significantly. Demand resources are receiving increasing attention as one means of providing the ancillary services. Control and coordination of a large number (~millions) of distributed smart grid assets requires innovative approaches. One such approach is transactive control and coordination (TC2)—a distributed, hierarchical, agent-based incentive and control system. The TC2 paradigm is to create a market-like control system in which participation is voluntary and the participant sets the price for participation. For transactions that are frequent, automation of bids and responses is necessary. Such an approach has been developed and demonstrated at the Pacific Northwest National Laboratory. The devices, typically thermostatically controlled heating, ventilating, and air conditioning (HVAC) loads, send their bids—the quantity of energy they need and, based on the consumer preferences encoded in a simple user interface, the price they are willing to pay. The typical bid period is 5 minutes. By aggregating all the bids, a demand curve is generated by the aggregating entity, and matched with a supply curve or supply constraint. The aggregator transmits the clearing price to the devices. The winning devices proceed to consume the energy they bid for and won. It is the purpose of this project to develop a similar approach for ancillary services. In this report, the following ancillary services are considered: spinning reserve, ramping, and regulation. These services are to be provided by the following devices: refrigerators, water heaters, clothes dryers, variable speed drives. We will assume that the variable speed drives operate an air handling fan in a commercial building.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2013

2012 wind technologies market report

Annual wind power capacity additions in the United States achieved record levels in 2012, motivated by the then-planned expiration of federal tax incentives at the end of 2012 and recent improvements in the cost and performance of wind power technology. At the same time, even with a short-term extension of federal tax incentives now in place, the U.S. wind power industry is facing uncertain times. It will take time to rebuild the project pipeline, ensuring a slow year for new capacity additions in 2013. Continued low natural gas prices, modest electricity demand growth, and limited near-term demand from state renewables portfolio standards (RPS) have also put a damper on industry growth expectations. In combination with global competition within the sector, these trends continue to impact the manufacturing supply chain. What these trends mean for the medium to longer term remains to be seen, dictated in part by future natural gas prices, fossil plant retirements, and policy decisions, although recent declines in the price of wind energy have boost ed the prospects for future growth

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2013

Assessment of Energy Efficiency Improvement in the United States Petroleum Refining Industry

Adoption of efficient process technologies is an important approach to reducing CO 2 emissions, in particular those associated with combustion. In many cases, implementing energy efficiency measures is among the most cost-effective approaches that any refiner can take, improving productivity while reducing emissions. Therefore, careful analysis of the options and costs associated with efficiency measures is required to establish sound carbon policies addressing global climate change, and is the primary focus of LBNL’s current petroleum refining sector analysis for the U.S. Environmental Protection Agency. The analysis is aimed at identifying energy efficiency-related measures and developing energy abatement supply curves and CO 2 emissions reduction potential for the U.S. refining industry. A refinery model has been developed for this purpose that is a notional aggregation of the U.S. petroleum refining sector. It consists of twelve processing units and account s for the additional energy requirements from steam generation, hydrogen production and water utilities required by each of the twelve processing units. The model is carbon and energy balanced such that crud e oil inputs and major refinery sector outputs (fuels) are benchmarked to 2010 data. Estimates of the current penetration for the identified energy efficiency measures benchmark the energy requirements to those reported in U.S. DOE 2010 data. The remaining energy efficiency potential for each of the measures is estimated and compared to U.S. DOE fuel prices resulting in estimates of cost- effective energy efficiency opportunities for each of the twelve major processes. A combined cost of conserved energy supply curve is also presented along with the CO 2 emissions abatement opportunities that exist in the U.S. petroleum refinery sector. Roughly 1,200 PJ per year of primary fuels savings and close to 500 GWh per y ear of electricity savings are potentially cost-effective given U.S. DOE fuel price forecasts. This represents roughly 70 million metric tonnes of CO 2 emission reductions assuming 2010 emissions factor for grid electricity. Energy efficiency measures resulting in an additional 400 PJ per year of primary fuels savings and close to 1,700 GWh per year of electricity savings, and an associated 24 million metric tonnes of CO 2 emission reductions are not cost-effective given the same assumption with respect to fuel prices and electricity emissions factors. Compared to the modeled energy requirements for the U.S. petroleum refining sector, the cost effective potential represents a 40% reduction in fuel consumption and a 2% reduction in electricity consumption. The non-cost-effective potential represents an additional 13% reduction in fuel consumption and an additional 7% reduction in electricity consumption. The relative energy reduction potentials are mu ch higher for fuel consumption than electricity consumption largely in part because fuel is the primary energy consumption type in the refineries. Moreover, many cost effective fuel savings measures would increase electricity consumption. The model also has the potential to be used to examine the costs and benefits of the other CO 2 mitigation options, such as combined heat and power (CHP), carbon capture, and the potential introduction of biomass feedstocks. However, these options are not addressed in this report as this report is focused on developing the modeling methodology and assessing fuels savings measures. These opportunities to further reduce refinery sector CO 2 emissions and are recommended for further research and analysis.

25 ENERGY STORAGE↗
DOE OSTI2012

Benchmarking Non-Hardware Balance-of-System (Soft) Costs for U.S. Photovoltaic Systems Using a Bottom-Up Approach and Installer Survey

This report presents results from the first U.S. Department of Energy (DOE) sponsored, bottom-up data-collection and analysis of non-hardware balance-of-system costs—often referred to as “business process” or “soft” costs—for residential and commercial photovoltaic (PV) systems. Annual expenditure and labor-hour-productivity data are analyzed to benchmark 2010 soft costs related to the DOE priority areas of (1) customer acquisition; (2) permitting, inspection, and interconnection; (3) installation labor; and (4) installer labor for arranging third-party financing. Annual expenditure and labor-hour data were collected from 87 PV installers. After eliminating outliers, the survey sample consists of 75 installers, representing approximately 13% of all residential PV installations and 4% of all commercial installations added in 2010. Including assumed permitting fees, in 2010 the average soft costs benchmarked in this analysis total $1.50/W for residential systems (ranging from $0.66/W to $1.66/W between the 20th and 80th percentiles). For commercial systems, the median 2010 benchmarked soft costs (including assumed permitting fees) are $0.99/W for systems smaller than 250 kW (ranging from $0.51/W to $1.45/W between the 20th and 80th percentiles) and $0.25/W for systems larger than 250 kW (ranging from $0.17/W to $0.78/W between the 20th and 80th percentiles). Additional soft costs not benchmarked in the present analysis (e.g., installer profit, overhead, financing, and contracting) are significant and would add to these figures. The survey results provide a benchmark for measuring—and helping to accelerate—progress over the next decade toward achieving the DOE SunShot Initiative’s soft-cost-reduction targets. We conclude that the selected non-hardware business processes add considerable cost to U.S. PV systems, constituting 23% of residential PV system price, 17% of small commercial system price, and 5% of large commercial system price (in 2010). These processes present significant opportunities for further cost reductions and labor-productivity gains.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2013

Low-Cost Phase Change Material for Building Envelopes

A low-cost PCM process consisting of conversion of fats and oils to PCM-range paraffins, and subsequent “encapsulation” of the paraffin using conventional plastic compounding/pelletizing equipment was demonstrated. The PCM pellets produced were field-tested in a building envelope application. This involved combining the PCM pellets with cellulose insulation, whereby 33% reduction in peak heat flux and 12% reduction in heat gain was observed (average summertime performance). The selling price of the PCM pellets produced according to this low-cost process is expected to be in the $\$ $1.50-$\$ $3.00/lb range, compared to current encapsulated PCM price of about $\$ $7.00/lb. Whole-building simulations using corresponding PCM thermal analysis data suggest a payback time of 8 to 16 years (at current energy prices) for an attic insulation retrofit project in the Phoenix climate area.

25 ENERGY STORAGE↗
DOE OSTI2012

Addressing Energy Demand through Demand Response. International Experiences and Practices

Demand response (DR) is a load management tool which provides a cost-effective alternative to traditional supply-side solutions to address the growing demand during times of peak electrical load. According to the US Department of Energy (DOE), demand response reflects “changes in electric usage by end-use customers from their normal consumption patterns in response to changes in the price of electricity over time, or to incentive payments designed to induce lower electricity use at times of high wholesale market prices or when system reliability is jeopardized.” 1 The California Energy Commission (CEC) defines DR as “a reduction in customers’ electricity consumption over a given time interval relative to what would otherwise occur in response to a price signal, other financial incentives, or a reliability signal.” 2 This latter definition is perhaps most reflective of how DR is understood and implemented today in countries such as the US, Canada, and Australia where DR is primarily a dispatchable resource responding to signals from utilities, grid operators, and/or load aggregators (or DR providers).

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2015

2014 Wind Technologies Market Report

Wind power capacity additions in the United States rebounded in 2014, and continued growth through 2016 is anticipated. Recent and projected near-term growth is supported by the industry’s primary federal incentive—the production tax credit (PTC)—which is available for projects that began construction by the end of 2014. Wind additions are also being driven by recent improvements in the cost and performance of wind power technologies, which have resulted in the lowest power sales prices ever seen in the U.S. wind sector. Growing corporate demand for wind energy and state-level policies play important roles as well. Expectations for continued technological advancements and cost reductions may further boost future growth. At the same time, the prospects for growth beyond 2016 are uncertain. The PTC has expired, and its renewal remains in question. Continued low natural gas prices, modest electricity demand growth, and limited near-term demand from state renewables portfolio standards (RPS) have also put a damper on growth expectations. These trends, in combination with increasingly global supply chains, have limited the growth of domestic manufacturing of wind equipment. What they mean for wind power additions through the end of the decade and beyond will be dictated in part by future natural gas prices, fossil plant retirements, and policy decisions.

17 WIND ENERGY↗
DOE OSTI2013

The Value of Energy Storage for Grid Applications

This analysis evaluates several operational benefits of electricity storage, including load-leveling, spinning contingency reserves, and regulation reserves. Storage devices were simulated in a utility system in the western United States, and the operational costs of generation was compared to the same system without the added storage. This operational value of storage was estimated for devices of various sizes, providing different services, and with several sensitivities to fuel price and other factors. Overall, the results followed previous analyses that demonstrate relatively low value for load-leveling but greater value for provision of reserve services. The value was estimated by taking the difference in operational costs between cases with and without energy storage and represents the operational cost savings from deploying storage by a traditional vertically integrated utility. The analysis also estimated the potential revenues derived from a merchant storage plant in a restructured market, based on marginal system prices. Due to suppression of on-/off-peak price differentials and incomplete capture of system benefits (such as the cost of power plant starts), the revenue obtained by storage in a market setting appears to be substantially less than the net benefit provided to the system. This demonstrates some of the additional challenges for storage deployed in restructured energy markets.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2014

2013 Wind Technologies Market Report

This report describes the status of the U.S. wind energy industry market in 2013, its trends, performance, market drivers and future outlook. Annual wind power capacity additions in the United States were modest in 2013, but all signals point to more-robust growth in 2014 and 2015. With the industry’s primary federal support—the production tax credit (PTC)—only available for projects that had begun construction by the end of 2013, the next couple years will see those projects commissioned. Near-term wind additions will also be driven by recent improvements in the cost and performance of wind power technologies. At the same time, the prospects for growth beyond 2015 are uncertain. The PTC has expired, and its renewal remains in question. Continued low natural gas prices, modest electricity demand growth, and limited near-term demand from state renewables portfolio standards (RPS) have also put a damper on industry growth expectations. These trends, in combination with increasingly global supply chains, continue to impact domestic manufacturing of wind equipment. What they mean for wind power additions through the end of the decade and beyond will be dictated in part by future natural gas prices, fossil plant retirements, and policy decisions. At the same time, recent declines in wind energy costs and prices and the potential for continued technological advancements have boosted future growth prospects.

17 WIND ENERGY↗
DOE OSTI2015

Nationwide Analysis of U.S. Commercial Building Solar Photovoltaic (PV) Breakeven Conditions

The commercial sector offers strong potential for solar photovoltaics (PV) owing to abundant available roof space suitable for PV and the opportunity to offset the sector's substantial retail electricity purchases. This report evaluated the breakeven price of PV for 15 different building types and various financing options by calculating electricity savings based on detailed rate structures for most U.S. utility territories (representing approximately two thirds of U.S. commercial customers). We find that at current capital costs, an estimated 1/3 of U.S. commercial customers break even in the cash scenario and approximately 2/3 break even in the loan scenario. Variation in retail rates is a stronger driver of breakeven prices than is variation in building load or solar generation profiles. At the building level, variation in the average breakeven price is largely driven by the ability for a PV system to reduce demand charges.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2015

Net Metering and Market Feedback Loops: Exploring the Impact of Retail Rate Design on Distributed PV Deployment

The substantial increase in deployment of customer-sited solar photovoltaics (PV) in the United States has been driven by a combination of steeply declining costs, financing innovations, and supportive policies. Among those supportive policies is net metering, which in most states effectively allows customers to receive compensation for distributed PV generation at the full retail electricity price. The current design of retail electricity rates and the presence of net metering have elicited concerns that the possible under-recovery of fixed utility costs from PV system owners may lead to a feedback loop of increasing retail prices that accelerate PV adoption and further rate increases. However, a separate and opposing feedback loop could offset this effect: increased PV deployment may lead to a shift in the timing of peak-period electricity prices that could reduce the bill savings received under net metering where time-varying retail electricity rates are used, thereby dampening further PV adoption. In this paper, we examine the impacts of these two competing feedback dynamics on U.S. distributed PV deployment through 2050 for both residential and commercial customers, across states. Our results indicate that, at the aggregate national level, the two feedback effects nearly offset one another and therefore produce a modest net effect, although their magnitude and direction vary by customer segment and by state. We also model aggregate PV deployment trends under various rate designs and net-metering rules, accounting for feedback dynamics. Our results demonstrate that future adoption of distributed PV is highly sensitive to retail rate structures. Whereas flat, time-invariant rates with net metering lead to higher aggregate national deployment levels than the current mix of rate structures (+5% in 2050), rate structures with higher monthly fixed customer charges or PV compensation at levels lower than the full retail rate can dramatically erode aggregate customer adoption of PV (from -14% to -61%, depending on the design). Moving towards time-varying rates, on the other hand, may accelerate near- and medium-term deployment (through 2030), but is found to slow adoption in the longer term (-22% in 2050).

14 SOLAR ENERGY↗
DOE OSTI2016

Capacity Payments in Restructured Markets under Low and High Penetration Levels of Renewable Energy

There is considerable debate about the degree to which restructured markets perform successfully in their use of capacity markets. In providing appropriate incentives for new and existing generation to meet reliability requirements, a variety of capacity market designs have developed across RTOs and ISOs in the United States and internationally. Growing levels of variable renewable energy (VRE) resources arguably create new challenges for capacity market designs, because VREs suppress energy prices while providing relatively little capacity, with these effects increase with VRE penetration. The purpose of this report is threefold. First, we provide a brief outline of the purpose and design of various capacity markets under consideration using variable resource requirement (VRR) demand curves. Second, we discuss some of the main challenges raised in existing literature and a set of interviews that we conducted with market participants, regulators, and observers, including where there substantive differences in opinion. Third, we consider some of the challenges that may be specific to higher penetration levels of VRE. While the well known 'merit order' effect from VRE can be expected to suppress wholesale energy prices and revenue, this may be partly mitigated by increased capacity payments and the greater importance of AS payments for flexible capacity. The potential for greater reliance on capacity markets for generator revenues may amplify any inefficiency and costs associated with capacity price volatility and other suboptimal market design choices. Regulatory intervention to ensure adequate capacity payments and ancillary service revenue may become more prevalent under current market designs as the timescale for market signals shifts increasingly from near term (e.g., day-ahead in wholesale electricity markets) to longer term (annual intervals in capacity markets). Our review and discussion with market participants suggest substantive challenges may remain in implementing capacity markets that provide both adequate operational and investment incentives, particularly under high-VRE scenarios with greater need for flexible capacity.

14 SOLAR ENERGY↗
DOE OSTI2016

Hydrothermal Liquefaction and Upgrading of Municipal Wastewater Treatment Plant Sludge: A Preliminary Techno-Economic Analysis

A preliminary process model and techno-economic analysis (TEA) was completed for fuel produced from hydrothermal liquefaction (HTL) of sludge waste from a municipal wastewater treatment plant (WWTP) and subsequent biocrude upgrading. The model is adapted from previous work by Jones et al. (2014) for algae HTL, using experimental data generated in fiscal year 2015 (FY15) bench-scale HTL testing of sludge waste streams. Testing was performed on sludge samples received from MetroVancouver’s Annacis Island WWTP (Vancouver, B.C.) as part of a collaborative project with the Water Environment and Reuse Foundation (WERF). The full set of sludge HTL testing data from this effort will be documented in a separate report to be issued by WERF. This analysis is based on limited testing data and therefore should be considered preliminary. Future refinements are necessary to improve the robustness of the model, including a cross-check of modeled biocrude components with the experimental GCMS data and investigation of equipment costs most appropriate at the smaller scales used here. Environmental sustainability metrics analysis is also needed to understand the broader impact of this technology pathway. The base case scenario for the analysis consists of 10 HTL plants, each processing 100 dry U.S. ton/day (92.4 ton/day on a dry, ash-free basis) of sludge waste and producing 234 barrel per stream day (BPSD) biocrude, feeding into a centralized biocrude upgrading facility that produces 2,020 barrel per standard day of final fuel. This scale was chosen based upon initial wastewater treatment plant data collected by the resource assessment team from the EPA’s Clean Watersheds Needs Survey database (EPA 2015a) and a rough estimate of what the potential sludge availability might be within a 100-mile radius. In addition, we received valuable feedback from the wastewater treatment industry as part of the WERF collaboration that helped form the basis for the selected HTL and upgrading plant scales and feedstock credit (current cost of disposal). It is assumed that the sludge is currently disposed of at $16.20/wet ton ($46/dry ton at 35% solids; $50/ton dry, ash-free basis) and this is included as a feedstock credit in the operating costs. The base case assumptions result in a minimum biocrude selling price of $3.8/gge and a minimum final upgraded fuel selling price of $4.9/gge. Several areas of process improvement and refinements to the analysis have the potential to significantly improve economics relative to the base case: • Optimization of HTL sludge feed solids content • Optimization of HTL biocrude yield • Optimization of HTL reactor liquid hourly space velocity (LHSV) • Optimization of fuel yield from hydrotreating • Combined large and small HTL scales specific to regions (e.g., metropolitan and suburban plants) Combined improvements believed to be achievable in these areas can potentially reduce the minimum selling price of biocrude and final upgraded fuel by about 50%. Further improvements may be possible through recovery of higher value components from the HTL aqueous phase, as being investigated under separate PNNL projects. Upgrading the biocrude at an existing petroleum refinery could also reduce the MFSP, although this option requires further testing to ensure compatibility and mitigate risks to a refinery. And finally, recycling the HTL aqueous phase product stream back to the headworks of the WWTP (with no catalytic hydrothermal gasification treatment) can significantly reduce cost. This option is uniquely appropriate for application at a water treatment facility but also requires further investigation to determine any technical and economic challenges related to the extra chemical oxygen demand (COD) associated with the recycled water.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2016

Hydrothermal Liquefaction and Upgrading of Municipal Wastewater Treatment Plant Sludge: A Preliminary Techno-Economic Analysis, Rev.1

A preliminary process model and techno-economic analysis (TEA) was completed for fuel produced from hydrothermal liquefaction (HTL) of sludge waste from a municipal wastewater treatment plant (WWTP) and subsequent biocrude upgrading. The model is adapted from previous work by Jones et al. (2014) for algae HTL, using experimental data generated in fiscal year 2015 (FY15) bench-scale HTL testing of sludge waste streams. Testing was performed on sludge samples received from Metro Vancouver’s Annacis Island WWTP (Vancouver, B.C.) as part of a collaborative project with the Water Environment and Reuse Foundation (WERF). The full set of sludge HTL testing data from this effort will be documented in a separate report to be issued by WERF. This analysis is based on limited testing data and therefore should be considered preliminary. In addition, the testing was conducted with the goal of successful operation, and therefore does not represent an optimized process. Future refinements are necessary to improve the robustness of the model, including a cross-check of modeled biocrude components with the experimental GCMS data and investigation of equipment costs most appropriate at the relatively small scales used here. Environmental sustainability metrics analysis is also needed to understand the broader impact of this technology pathway. The base case scenario for the analysis consists of 10 HTL plants, each processing 100 dry U.S. ton/day (92.4 ton/day on a dry, ash-free basis) of sludge waste and producing 234 barrel per stream day (BPSD) biocrude, feeding into a centralized biocrude upgrading facility that produces 2,020 barrel per standard day of final fuel. This scale was chosen based upon initial wastewater treatment plant data collected by PNNL’s resource assessment team from the EPA’s Clean Watersheds Needs Survey database (EPA 2015a) and a rough estimate of what the potential sludge availability might be within a 100-mile radius. In addition, we received valuable feedback from the wastewater treatment industry as part of the WERF collaboration that helped form the basis for the selected HTL and upgrading plant scales and feedstock credit (current cost of disposal). It is assumed that the sludge is currently disposed of at $16.20/wet ton ($46/dry ton at 35% solids; $50/ton dry, ash-free basis) and this is included as a feedstock credit in the operating costs. The base case assumptions result in a minimum biocrude selling price of $3.8/gge and a minimum final upgraded fuel selling price of $4.9/gge. Several areas of process improvement and refinements to the analysis have the potential to significantly improve economics relative to the base case: •Optimization of HTL sludge feed solids content •Optimization of HTL biocrude yield •Optimization of HTL reactor liquid hourly space velocity (LHSV) •Optimization of fuel yield from hydrotreating •Combined large and small HTL scales specific to regions (e.g., metropolitan and suburban plants) Combined improvements believed to be achievable in these areas can potentially reduce the minimum selling price of biocrude and final upgraded fuel by about 50%. Further improvements may be possible through recovery of higher value components from the HTL aqueous phase, as being investigated under separate PNNL projects. Upgrading the biocrude at an existing petroleum refinery could also reduce the MFSP, although this option requires further testing to ensure compatibility and mitigation of risks to a refinery. And finally, recycling the HTL aqueous phase product stream back to the headworks of the WWTP (with no catalytic hydrothermal gasification treatment) can significantly reduce cost. This option is uniquely appropriate for application at a water treatment facility but also requires further investigation to determine any technical and economic challenges related to the extra chemical oxygen demand (COD) associated with the recycled water.

09 BIOMASS FUELS↗
DOE OSTI2016

Marine Hydrokinetic Energy Site Identification and Ranking Methodology Part I: Wave Energy

Marine hydrokinetic energy is a promising and growing piece of the renewable energy sector that offers high predictability and additional energy sources for a diversified energy economy. This report investigates the market opportunities for wave energy along the U.S. coastlines. It is part one of a two-part investigation into the United State's two largest marine hydrokinetic resources (wave and tidal). Wave energy technology is still an emerging form of renewable energy for which large-scale, grid-connected project costs are currently poorly defined. Ideally, device designers would like to know the resource conditions at economical project sites so they can optimize device designs. On the other hand, project developers need detailed device cost data to identify sites where projects are economical. That is, device design and siting are, to some extent, a coupled problem. This work describes a methodology for identifying likely deployment locations based on a set of criteria that wave energy experts in industry, academia, and national laboratories agree are likely to be important factors for all technology types. This work groups the data for the six criteria into 'locales' that are defined as the smaller of either the local transmission grid or a state boundary. The former applies to U.S. islands (e.g., Hawaii, American Samoa) and rural villages (e.g., in Alaska); the latter applies to states in the contiguous United States. These data are then scored from 0 to 10 according to scoring functions that were developed with input from wave energy industry and academic experts. The scores are aggregated using a simple product method that includes a weighting factor for each criterion. This work presents two weighting scenarios: a long-term scenario that does not include energy price (weighted zero) and a near term scenario that includes energy price. The aggregated scores are then used to produce ranked lists of likely deployment locales. In both scenarios, Hawaii and the Pacific Northwest (northern California, Oregon, and Washington) rank at the top of the lists. Hawaii ranks highest in the near-term scenario because it has high energy costs. In the long-term scenario, Oregon ranks highest because it has a large market and an energetic resource. Several East Coast states and Puerto Rico are also identified as potential wave energy deployment sites if technological innovations make it possible to efficiently generate electricity from the modest resource there. There are also several small-market sites in Alaska and U.S. Pacific Islands that rank particularly well in the near-term analysis due to their high energy prices. These locations may represent opportunities to demonstrate economical wave energy conversion as a stepping-stone to larger markets. Several factors that will affect wave project costs and siting have not been considered here -- including permitting constraints, conflicting use, seasonal resource variability, extreme event likelihood, and distance to ports -- because consistent data are unavailable or technology-independent scoring could not be identified. As the industry continues to mature and converge around a subset of device archetypes with well-defined costs, more precise investigations of project siting that include these factors will be possible. For now, these results provide a high-level guide pointing to the regions where markets and resource will one day support commercial wave energy projects.

16 TIDAL AND WAVE POWER↗
DOE OSTI2016

Outlooks for Wind Power in the United States: Drivers and Trends under a 2016 Policy Environment

Over the past decade, wind power has become one of the fastest growing electricity generation sources in the United States. Despite this growth, the U.S. wind industry continues to experience year-to-year fluctuations across the manufacturing and supply chain as a result of dynamic market conditions and changing policy landscapes. Moreover, with advancing wind technologies, ever-changing fossil fuel prices, and evolving energy policies, the long-term future for wind power is highly uncertain. In this report, we present multiple outlooks for wind power in the United States, to explore the possibilities of future wind deployment. The future wind power outlooks presented rely on high-resolution wind resource data and advanced electric sector modeling capabilities to evaluate an array of potential scenarios of the U.S. electricity system. Scenario analysis is used to explore drivers, trends, and implications for wind power deployment over multiple periods through 2050. Specifically, we model 16 scenarios of wind deployment in the contiguous United States. These scenarios span a wide range of wind technology costs, natural gas prices, and future transmission expansion. We identify conditions with more consistent wind deployment after the production tax credit expires as well as drivers for more robust wind growth in the long run. Conversely, we highlight challenges to future wind deployment. We find that the degree to which wind technology costs decline can play an important role in future wind deployment, electric sector CO 2 emissions, and lowering allowance prices for the Clean Power Plan.

17 WIND ENERGY↗
DOE OSTI2017

Analyzing The Impacts of the Biogas-to-Electricity Purchase Incentives on Electric Vehicle Deployment with the MA3T Vehicle Choice Model

This analysis represents the biogas-to-electricity pathway under the Renewable Fuel Standard (RFS) as a point of purchase incentive and tests the impact of this incentive on EV deployment using a vehicle consumer choice model. The credit value generated under this policy was calculated in a number of scenarios based on electricity use of each power train choice on a yearly basis over the 15 year vehicle lifetime, accounting for the average electric vehicle miles travelled and vehicle efficiency, competition for biogas-derived electricity among electric vehicles (EVs), the RIN equivalence value and the time value of money. The credit value calculation in each of these scenarios is offered upfront as a point of purchase incentive for EVs using the Market Acceptance of Advanced Automotive Technologies (MA3T) vehicle choice model, which tracks sales, fleet size and energy use over time. The majority of the scenarios use a proposed RIN equivalence value, which increases the credit value as a way to explore the analysis space. Additional model runs show the relative impact of the equivalence value on EV deployment. The MA3T model output shows that a consumer incentive accelerates the deployment of EVs for all scenarios relative to the baseline (no policy) case. In the scenario modeled to represent the current biogas-to-electricity generation capacity (15 TWh/year) with a 5.24kWh/RIN equivalence value, the policy leads to an additional 1.4 million plug-in hybrid electric vehicles (PHEVs) and 3.5 million battery electric vehicles (BEVs) in 2025 beyond the no-policy case of 1.3 million PHEVs and 2.1 million BEVs when the full value of the credit is passed on to the consumer. In 2030, this increases to 2.4 million PHEVs and 7.3 million BEVs beyond the baseline. This larger impact on BEVs relative to PHEVs is due in part to the larger credit that BEVs receive in the model based on the greater percentage of electric vehicle miles traveled by BEVs relative to PHEVs. In this scenario 2025 also represents the last year in which biogas-derived electricity is able to fully supply the transportation electricity demand in the model. After 2025, the credit value declines on a per vehicle basis. At the same time a larger fraction of the credit may shift towards biogas producers in order to incent additional biogas production. The expanded 41 TWh/year biogas availability scenarios represent an increase beyond today s generation capacity and allow greater eRIN generation. With a 5.24kWh/RIN equivalence value, when all of the credit is directed towards reducing vehicle purchase prices, the 41 TWh/year biogas scenario results in 4.1 million additional PHEVs and 12.2 million additional BEVs on the road in 2030 beyond the baseline of 2.5 million PHEVs and 6.1 million BEVs. Under this expanded biogas capacity, biogas-derived electricity generation is able to fully supply electricity for a fleet of over 21 million EVs (15.6 million BEVs and 5.8 million PHEVs) on a yearly basis. In addition to assessing the full value credit scenarios described above, multiple scenarios were analyzed to determine the impact if only a fraction of the credit value was passed on to the consumer. In all of these cases, the EV deployment was scaled back as the fraction of the credit that was passed on to the consumer was reduced. These scenarios can be used to estimate the impact if the credit value is reduced in other ways as well, as demonstrated by the scenarios where the current (22.6 kWh/RIN) equivalence value was used. The EV deployment that results from an equivalence value of 22.6 kWh/RIN equivalence value is roughly equivalent to the EV deployment observed in the 25% case using the 5.24 kWh/RIN equivalence value. A higher equivalence value means that a smaller number of credits, and therefore value, is created for each kWh, and therefore the impact on EV deployment is reduced. This analysis shows several of the drivers that will impact eRIN generation and credit value, and tests the impact of an eRIN point of purchase incentive on EV deployment. This additional incentive can accelerate the deployment of EVs when it is used to reduce vehicle purchase prices. However, the ultimate impact of this policy, as modeled here, will be determined by future RIN prices, the extent to which eRIN credit value can be passed on to the consumer as a point of purchase incentive and the equivalence value.

09 BIOMASS FUELS↗